Austin, Texas
Experience: 5 - 8 Years
Required Skills & Qualifications
• Bachelor's or Master's degree in Finance, Data Science, Business Analytics, or related field.
• 5+ years of experience in a data analyst role within wealth management, asset management, or financial services.
• Expert-level SQL skills — complex multi-table joins, CTEs, window functions, subqueries, and analytical query design.
• Strong ability to gather and analyze functional requirements from business stakeholders and translate them into data logic and acceptance criteria.
• Proven experience with data discovery and profiling — understanding data structures, identifying quality issues, and documenting findings clearly.
• Experience validating data pipelines or ETL outputs — reconciling source vs. target data, verifying business logic, and writing test cases.
• Solid understanding of wealth management data — custodian feeds, portfolio holdings, performance returns, AUM, fees, and transactions.
• Proficiency with Python for data analysis and ad hoc exploration (pandas, numpy); PySpark experience is a plus.
• Familiarity with Databricks or similar cloud data platforms for querying and analyzing large datasets.
• Understanding of data governance, data quality frameworks, and regulatory compliance in financial services.
• Excellent communication and stakeholder management skills — comfortable presenting findings to both technical and business audiences.
Preferred Qualifications
• Hands-on experience with PySpark or Databricks (Delta Lake, Spark SQL, notebooks) for large-scale data processing.
• Experience building or contributing to data pipelines, ETL processes, or workflow automation in a financial services context.
• Exposure to custodian data formats and feeds (Schwab, Pershing, Fidelity, etc.) and reconciliation processes.
• Experience with wealth management or portfolio management platforms such as Addepar, Orion, or Black Diamond.
• Familiarity with cloud data platforms such as AWS, Azure, or Snowflake.
• Knowledge of predictive analytics or basic ML applications in financial services (e.g., client segmentation, risk modeling).
• Certifications in data analytics, financial analysis (CFA, CIPM), or cloud platforms are a plus.